Process for mapping multiple-bounce ghosting artifacts from radar imaging data

ABSTRACT

Described herein are frequency-domain back-projection processes for forming spotlight synthetic aperture radar (“SAR”) images that are not corrupted by the effects of multiple-bounce ghosting artifacts. These processes give an approximately exact reconstruction of the multiple bounce reflectivity function (MBRF) ƒ(x,y,γ). Specifically, the evaluation of ƒ(x,y,γ) in they γ=0 plane gives an approximately exact reconstruction of the true object scattering centers which is uncorrupted by multiple-bounce contributions to the phase history data G(ξ, θ). In addition, the non-zero dependence of ƒ(x,y,γ) upon the MB coordinate γ can be used to facilitate the identification of features-interest within the imaged region.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and incorporates by reference inits entirety, U.S. patent application Ser. No. 10/631,712 filed Aug. 1,2003, now U.S. Pat. No. 6,812,886, entitled “PROCESS FOR MAPPINGMULTIPLE-BOUNCING GHOSTING ARTIFACTS FROM RADAR IMAGING DATA,” whichclaims priority to U.S. patent application Ser. No. 10/059,416 entitled“PROCESS FOR MAPPING MULTIPLE-BOUNCE GHOSTING ARTIFACTS FROM RADARIMAGING DATA,” now U.S. Pat. No. 6,646,593, which claims priority to andincorporates by reference in its entirety, U.S. Provisional PatentApplication No. 60/345,639, entitled “SPOTLIGHT SAR IMAGE FORMATIONWITHOUT MULTIPLE-BOUNCE GHOSTING ARTIFACTS” filed Jan. 8, 2002.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Generally, the invention involves a process for forming radar images.More specifically, the invention involves a process for mapping multiplebounce ghosts (MBGs) and for forming radar images without thedeleterious effects of MBGs.

2. Description of the Related Art

Radar, at its most basic application, is used to measure the range to atarget. With knowledge of the speed of propagation of the wave, i.e.,electromagnetic wave, that is transmitted toward the target, it ispossible to resolve in a first dimension, the distance to the target,based on the received reflected wave or echo. In order to use radar asan imaging tool, it is necessary to collect information about thecross-range of the target, in addition to the first dimensioninformation. This cross-range information is about a second dimensionperpendicular to the first dimension.

Synthetic aperture radar (SAR) can be used to collect data in both thefirst and second dimensions, through a process wherein the reflectedwaves are measured at different angles with respect to anobject-of-interest. This process is referred to in the art as collectingradar measurements over a synthetic (as opposed to a literal) aperture.By taking various measurements of the object-of-interest from varyingaspect angles, it is possible to determine approximate distance to thescattering centers within an object-of-interest in the first dimensionand location of these scattering centers within the object-of-interestin the second, cross-range dimension. This process of two-dimensionalimaging is commonly referred to as reflection tomography.

SAR systems take advantage of the long-range propagation characteristicsof radar signals and the complex information processing capability ofmodern digital electronics to provide high-resolution imagery. SARimaging is not restricted by time of day or atmospheric conditions dueto its operative frequencies. Consequently, SAR imaging supplementsother photographic and optical imaging techniques in order to facilitateenvironmental monitoring, earth-resource mapping, and militaryoperations which may require broad-area imaging at high resolutions.More specifically, SAR technology provides detailed terrain informationto geologists for mineral exploration, environmentalists fordetermination of oil spill boundaries, navigators for sea state and icehazard mapping, and the military for reconnaissance and targetinginformation.

Other systems using reflection data, also referred to as projectionmeasurements, are fault inspection systems using acoustic imaging,submarine sonar for imaging underwater objects and the like, seismicimaging system for tunnel detection, oil exploration, geologicalsurveys, etc., and medical diagnostic tools such as sonograms andechocardiograms.

There have been two basic types of processing techniques used in thefield of reflection tomography to reconstruct single-bounce (SB)reflection data. First, the frequency-domain projection-slice theoremtakes the measured phase history from the reflection data taken atdifferent aspect angles and generates the reconstruction of an imageusing Fourier transforms. This reconstruction technique is often usedfor reconstructing SAR image data in order to minimize the computationalload that results from necessarily complex processing. A secondtechnique, more prevalent in the medical imaging community, is based onthe time-domain back projection techniques. Both of these techniques arediscussed in U.S. Pat. No. 5,805,098 to McCorkle which is incorporatedherein by reference in its entirety.

SUMMARY OF THE INVENTION

Summary of the Problem

The reflection data processing techniques described in the related artassume that the impinging wave reflects a single time off of the objectof interest before returning back to the receiver. This assumptionneglects the situation wherein the wave actually reflects off ofmultiple portions of the object of interest, cascading for any number oftimes, before returning to the receiver. All prior art on reflectiontomography assumes only one bounce. That is, conventional tomographydoes not include the effects of multiple-bounce (MB) scattering eventswherein the mediating waveform first scatters off of one portion of theextended object-of-interest, which then scatters in a cascade fashionoff of one or more other regions of this same extended object-ofinterest or off of other objects before scattering into the receiver.These facts motivate the need for the development of a modified imageformation process that applies for cases in which the measuredreflection data also identifies and accounts for MB scattering events.

A particular imaging scenario wherein the prior art process isinsufficient includes determining the physical distribution of thescattering centers for cases in which multiple-bounce echoes occur. Forexample, prior art processes would have a difficult time determiningfrom three (3) received echoes that instead of three scattering centersfor an object-of-interest, the image actually contained two targets,wherein the third echo was between the two scattering centers as opposedto a third scattering center. The problem is exacerbated in a case wherethere are many scattering centers within the image, with the possibilityfor multiple bounces between the scattering centers. Further, in thecase of SAR imaging, the process becomes even more complicated by thefact that the object (or region)-of-interest radar information is onlygathered over a very small aspect angle range, for example, up toapproximately 15 degrees. Current processing schemes place themultiple-bounce echoes at incorrect (ghost) locations due to fundamentalassumptions implicit in the processing.

Summary of the Solution

Described herein are frequency-domain back-projection processes forforming, e.g, spotlight SAR images that are not corrupted by the effectsof multiple-bounce ghosting artifacts. These processes give anapproximately exact reconstruction of the multiple bounce reflectivityfunction (MBRF) ƒ(x,y,γ). As shown in the embodiments herein, thereconstruction process is not affected to any greater extent by noiseand interference than in the case of the single bounce processing.Specifically, the evaluation of ƒ(x,y,γ) in the γ=0 plane gives anapproximately exact reconstruction of the true object scattering centerswhich is uncorrupted by multiple-bounce contributions to the phasehistory data G(ξ, θ). In addition, the non-zero dependence of ƒ(x,y,γ)upon the MB coordinate γ can be used to facilitate the identification offeatures-interest within the imaged region.

In an embodiment of the present invention, a process is described forincorporating the effects of multiple bounces obtained using the backprojection imaging process referred to herein, in order to obtain acleaned up image that has removed the misleading effects of the multiplebounces, commonly referred to as multiple bounce ghosts (MBGs). Theprocess identifies the MBGs and maps the MBG reflections into ametrically-correct image plane, also referred to as, a delay image planeor higher dimensional image plane, creating auxiliary images, which areuseful in providing further information about the viewing region,including additional discriminating information that could assist inobject recognition and identification. The imaging information,including both single and multiple bounce information, is only requiredfor a narrow range of measurement angles.

The processes of the present invention are useful in three dimensionalimaging, providing images that are free of multi-path effects, andextending the field-of-use of the technology beyond SAR imaging to allareas of reflection tomography. Because the technology incorporates aphysical model that explicitly allows for multi-bounce effects in imageformation, it is applicable to any image formation technology where thatphenomenon is found such as, but not limited to, real aperture radarimaging, synthetic aperture radar (SAR) imaging, inverse SAR (ISAR)imaging, active sonar underwater acoustic mapping, active geographicacoustic exploration and ultrasonic medical imaging.

BRIEF DESCRIPTION OF THE DRAWINGS

In the Figures:

FIGS. 1( a)–1(b) illustrate scattering events according to embodimentsof the present invention;

FIGS. 2( a)–2(d) illustrate true scattering point data from multipledepth planes, according to a first embodiment of the present invention;

FIGS. 3( a)–3(b) illustrate measurement functions for scattering dataaccording to a first embodiment of the present invention;

FIG. 4 illustrates scattering point data for an object-of-interestutilizing single bounce reconstruction, according to a first embodimentof the present invention;

FIGS. 5( a)–5(d) illustrate scattering point data for anobject-of-interest utilizing multiple bounce reconstruction according toa first embodiment of the present invention;

FIGS. 6( a)–6(d) illustrate true scattering point data from multipledepth planes, according to a second embodiment of the present invention;

FIGS. 7( a)–7(b) illustrate measurement functions for scattering dataaccording to a second embodiment of the present invention;

FIG. 8 illustrates scattering point data for an object-of-interestutilizing single bounce reconstruction, according to a second embodimentof the present invention; and

FIGS. 9( a)–9(d) illustrate scattering point data for anobject-of-interest utilizing multiple bounce reconstruction according toa second embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE PRESENTINVENTION

In a preferred embodiment of the present invention, the imaging processassumes the condition that a scattered waveform has the samecharacteristics as the incident waveform. In the context of SAR imaging,the measured range to target (i.e., assumed object-of-interest) of agiven multiple bounce (MB) scattering event at some aperture angle isidentical to that of an equivalent single bounce (SB) scattering event.FIG. 1 a presents an example of such equivalent scattering events. Inthis example, the transmitter is co-located with the receiver (bothshown as 10) at a large distance from the region-of-interest, so thatthe effects of a curved wave front can be neglected. One skilled in theart recognizes that the process described herein may be adjusted for nonco-located transmitter and receiver configurations, as well as fordistances from the region of interest which result in curved wavefronts.FIG. 1 a shows that waveform traverses a total distance of 2 L inpropagating from the transmitter at 10 to scattering point (SP) 1 andback to the co-located receiver at 10. This distance represents themeasured range-to target from the co-located transmitter-receiver to SP1 based upon waveform transmission and the corresponding reception ofthe its echo.

The specifics of the transmitter and receiver configurations usable withthe embodiments described herein are known to those skilled in the art.Further, one skilled in the art recognizes that the mode of collectionof the SAR data is not limited to any single mode, but may include,among others, spotlight mode, strip map mode, range Doppler profiler(RDP) mode, air-to-air mode, and the like.

Still referring to FIG. 1 a, the multiple scattering event due to SPs 2and 3 are considered. Here, the transmitted waveform reflects off of SP2, which then reflects off of SP 3 prior to reception. If the sum of theline segments A+B+C is equal to 2 L in FIG. 1 a, then the measuredrange-to target of the single-scattering event at SP 1 at the shownmeasurement angle θ is identical to, and therefore indistinguishablefrom, the double-scattering event due to the cascaded reflection from SP2 to SP 3. In addition, multiple-scattering events involving three ormore scattering points can have this same range-to-target, as shown inFIG. 1 b.

Referring to FIG. 1 b, the sum of the two-point scattering event, A+B+C,cascaded from SP 2 and SP 3 and measured at the receiver 10, isequivalent to the sum of the three-point scattering event, D+E+F+G,cascaded from SP 4, SP 5, and SP 6 measured at the receiver 10, at theshown measurement angle θ.

As described in the Summary of the Problem, conventional SAR theoryassumes that all scattering events which affect the measured phasehistory data (i.e., range-to-target projections) are due to SB effects,as occurs with SP 1 in FIG. 1 a. This is not always the case. When MBevents, such as SPs 2 and 3 in FIG. 1 a, also significantly impact themeasured phase history data, the application of the conventional SARimage formation techniques yield imperfect reconstructions of theregion-of-interest.

According to an embodiment of the present invention, the best image ofthe true scattering centers is obtained by taking into account the MBGdata retrieved via imaging through application of an imagereconstruction algorithm for multi-bounce scattering (IRAMS). Thisprocess for applying the IRAMS is developed through examination of theproperties of the MB scattering event shown in, for example, FIG. 1 a.The data, retrieved via the transmitter and receiver configurations 10,is processed according to the IRAMS via a processor (not shown).Processors are well known in the art and will not be discussed furtherherein. Let (x_(i), y_(i)) denote the two-dimensional (2-D) Cartesiancoordinates of the spatial position of SP 2 with which the incidentwaveform initially interacts. Define (x_(ƒ), y_(ƒ)) to be the spatialposition of SP 3 with which the waveform that was initially reflectedoff of SP 2 interacts before being reflected into the receiver. Let thevariable {tilde over (γ)} denotes the effective path length that thewaveform traverses between the initial (x_(i), y_(i)) and final (x_(ƒ),y_(ƒ)) scattering centers. This path length {tilde over (γ)} is notrequired to be equal to the distance between the initial and finalscattering centers, i.e., √{square root over((x_(ƒ)−x_(i))²+(y_(ƒ)−y_(i))²)}{square root over((x_(ƒ)−x_(i))²+(y_(ƒ)−y_(i))²)}, as occurs for scattering eventsinvolving only two scattering centers. For example, triple-pointscattering events are possible, as shown in FIG. 1 b. FIG. 1 b shows awaveform scattering off of the initial point (x_(i), y_(i)) of SP 4 toan intermediate location (x₅, y₅) at SP 5, before reflecting off of thefinal scattering center (x_(ƒ), y_(ƒ)) at SP 6 and subsequently beingcollected into the receiver.

Under the definitions above, all single-reflection scattering events arecharacterized by {tilde over (γ)}=0. Double-reflection scattering eventshave {tilde over (γ)}=√{square root over((x_(ƒ)−x_(i))²+(y_(ƒ)−y_(i))²)}{square root over((x_(ƒ)−x_(i))²+(y_(ƒ)−y_(i))²)}, whereas N-point scattering events withN>2 are characterized by {tilde over (γ)}>√{square root over((x_(ƒ)−x_(i))²+(y_(ƒ)−y_(i))²)}{square root over((x_(ƒ)−x_(i))²+(y_(ƒ)−y_(i))²)}. Again, the goal of this analysis is toremove the effects of all MB scattering events, which are described by{tilde over (γ)}>0, in order to form an improved spotlight SAR image.

FIGS. 1 a and 1 b reveal that the measured range-to target of ascattering event relative to that of the coordinate origin (x=0, y=0)depends upon the observation angle θ vias(θ)=[{x _(i) +x _(ƒ)}cos(θ)+{y _(i) +y _(ƒ)} sin(θ)+{tilde over(γ)}]/2.  (1)The division by 2 arises since the range-to-target is equal to one-halfof the round-trip distance that the waveform traverses from thetransmitter to the object and back into the co-located receiver. Therange-to-target spatial coordinate s is related to the waveformtime-of-flight t via

$\begin{matrix}{{t = \frac{2s}{c}},} & (2)\end{matrix}$with c equal to the waveform speed. Given the MB range-to-targetmeasurements s(θ) collected over some extent of angles θ for an isolatedscattering event, it is clear from Eq.(1) that it is possible todetermine only the mean position of the initial and final scatteringcenters, i.e., {(x_(i)+x_(ƒ))/2, (y_(i)+y_(ƒ))/2}, and the total pathlength {tilde over (γ)} between these two points. No other informationcan be unambiguously gleaned.

Since only the mean position of the initial and final scattering centerscan be determined uniquely, the measured range-to-target for a N-pointscattering event with respect to the selected coordinate origin can beexpressed in the forms(θ)=x cos(θ)+y sin(θ)+γ,  (3)in terms of the parameters {x≡(x_(i)+x_(ƒ))/2, y≡{y_(i)+y_(ƒ)},γ≡{{tilde over (γ)}/2} of Eq. (1). Every N-point scattering event ischaracterized by the parameters (x,y,γ) and traces a sinusoid inrange-angle (s, θ) that is shifted in the s-direction by a distance ofγ. In contrast, each SB scattering event is characterized by theparameters (x,y; γ=0) and traces an unshifted sinusoid in range-anglespace.

Equation (3) shows that for any generic N-point scattering event withN>1, range-to-target measurements can yield only the values (x,y,γ).This result applies even if there are no other scattering eventspresent. In fact, many individual scattering events having one, two, ormore bounces can have identical values of (x,y,γ). Specifically, allscattering events having the same average position for the initial andfinal scattering centers, i.e., x=(x_(i)+x_(ƒ))/2 andy≡{y_(i)+y_(ƒ)}/2}, and having the same total path length {tilde over(γ)}˜2γ between the initial and final scattering centers contribute tothe same (x,y,γ)-point in the MB reflectivity function (MBRF) densityƒ(x,y,γ). That is, the density ƒ(x,y,γ) at a given point in(x,y,γ)-space is formed by the integral of all N-point scattering eventscharacterized by the same values of x, y, and γ. In general, the MBRFƒ(x,y,γ) is permitted to be a complex function, as in the modeling ofthe single-bounce reflectivity function ƒ_(SB)(x,y).

Consider the two different scattering events presented in FIG. 1 b. Therange-to-target of the transmitted waveform is considered with respectto the parallel wave front denoted by the thin dashed lines in thisfigure. The line segments have been drawn so that the followingrelations hold:A+B+C=D+E+F+G,  (4)x ₂ +x ₃ =x ₄ +x ₆  (5)y ₂ +y ₃ =y ₄ +y ₆  (6)Thus, the 2-point scattering event of SPs 2 and 3, and the 3-pointscattering event of SPs 4, 5, and 6 have the same values of x, y, and γwithin the MBRF ƒ(x,y,γ). The MBRF is the most basic representation ofthe region-of-interest which can be reconstructed based upon phasehistory measurements that include contributions due to MB scatteringevents.

The measured MB projection function g(s, θ) is determined by the MBRFƒ(x,y,γ) via

$\begin{matrix}{{{g\left( {s,\theta} \right)} \equiv {\int_{\infty}^{\infty}\ {{\mathbb{d}x}{\int_{\infty}^{\infty}\ {{\mathbb{d}y}{\int_{\infty}^{\infty}\ {{\mathbb{d}\gamma}\;{f\left( {x,y,\gamma} \right)}{\delta\left( {{x\;{\cos(\theta)}} + {y\;{\sin(\theta)}} + \gamma - s} \right)}}}}}}}},} & (7)\end{matrix}$with δ( . . . ) the standard Delta function defined byA(s)≡∫ds′A(s′)δ(s′−s). That is, Eq. (7) is merely the integral of allrange-to-target projections of MB scattering events, as determined byEq. (3). The integral over dγin Eq. (7) applies from −∞ to ∞, providedthat the MBRF ƒ(x,y,γ) is constrained to be equal to zero for γ<0. Ifthe MBRF density contains only single-bounce effects, i.e.,ƒ_(SB)(x,y)≡ƒ(x,y; γ=0), then the MB transformation equation (7) reducesto the conventional Radon transform

$\begin{matrix}{{g_{SB}\left( {s,\theta} \right)} \equiv {\int_{\infty}^{\infty}\ {{\mathbb{d}x}{\int_{\infty}^{\infty}\ {{\mathbb{d}y}\;{f_{SB}\left( {x,y} \right)}{\delta\left( {{x\;{\cos(\theta)}} + {y\;{\sin(\theta)}} - s} \right)}}}}}} & (8)\end{matrix}$

Define the 1-D Fourier transform of the function g(s, θ) with respect tos, applied independently for each observation angle θ, i.e.,

$\begin{matrix}{{G\left( {\xi,\;\theta} \right)} \equiv {\int_{\infty}^{\infty}\ {{\mathbb{d}s}\;{\exp\left( {{- {j2\pi}}\; s\;\xi} \right)}{{g\left( {s,\theta} \right)}.}}}} & (9)\end{matrix}$The notation g(s, θ)

G(ε, θ) expresses this relationship. The measurements for G(ε, θ) in Eq.(9) are collected over some finite interval with respect to the spatialfrequency variable, i.e., ξ_(min)≦ξ≦ξ_(max), and some finite interval inthe observation angle, i.e. θ_(min)≦θ≦θ_(max). The spatial frequencycoordinate ε is related to the waveform temporal frequency coordinate τvia

$\begin{matrix}{{\tau = {\frac{c}{2}\xi}},} & (10)\end{matrix}$so that the waveform temporal bandwidth is

$\begin{matrix}{{\Delta\;\tau} = {{- \frac{c}{2}}{\left\{ {\xi_{\max} - \xi_{\min}} \right\}.}}} & (11)\end{matrix}$Inserting Eq. (7) into Eq. (9) and interchanging the order ofintegration gives

$\begin{matrix}{{G\left( {\xi,\;\theta} \right)} \equiv {\int_{\infty}^{\infty}\ {{\mathbb{d}x}{\int_{\infty}^{\infty}\ {{\mathbb{d}y}{\int_{\infty}^{\infty}\ {{\mathbb{d}\gamma}\;{f\left( {x,y,\gamma} \right)}{\int_{\infty}^{\infty}\ {{\mathbb{d}s}\;{\exp\left( {{- {j2\pi}}\; s\;\xi} \right)}{{\delta\left( {{x\;{\cos(\theta)}} + {y\;{\sin(\theta)}} + \gamma - s} \right)}.}}}}}}}}}} & (12)\end{matrix}$Evaluation of the delta function yields the forward MB transformationbetween the MBRF ƒ(x,y,γ) and the phase history measurements G(ξ, θ):

$\begin{matrix}{{G\left( {\xi,\;\theta} \right)} \equiv {\int_{\infty}^{\infty}\ {{\mathbb{d}x}{\int_{\infty}^{\infty}\ {{\mathbb{d}y}{\int_{\infty}^{\infty}\ {{\mathbb{d}\gamma}\;{f\left( {x,y,\gamma} \right)}{{\exp\left( {{- {{j2\pi}\left\lbrack {{x\;{\cos(\theta)}} + {y\;{\sin(\theta)}} + \gamma} \right\rbrack}}\xi} \right)}.}}}}}}}} & (13)\end{matrix}$This equation implicitly assumes that the MBRF ƒ(x,y,γ) is invariantover the extent of measurement angles θ and spatial frequencies ξ underconsideration. In a similar fashion, the omission of MB effects yieldsthe corresponding SB result:

$\begin{matrix}{{{G_{SB}\left( {\xi,\theta} \right)} \equiv {\int_{\infty}^{\infty}\ {{\mathbb{d}x}{\int_{\infty}^{\infty}\ {{\mathbb{d}y}\;{f_{SB}\left( {x,y} \right)}{\exp\left( {{- {j2}}\;{\pi\left\lbrack {{x\;{\cos(\theta)}} + {y\;{\sin(\theta)}}} \right\rbrack}\xi} \right)}}}}}},} & (14)\end{matrix}$with G_(SB)(ξ, θ)

g_(SB)(s, θ).

There are two different classes of techniques for inverting Eq. (14) inorder to obtain the SB reflectivity function density ƒ_(SB)(x,y) giventhe measured projections G_(SB)(ξ, θ). The first class of methodsinvokes the projection-slice theorem. However, it is not clear thatthese projection-slice techniques can be utilized to invert theparticular MB transformation equation (13) of the present embodiment. Inparticular, the measurements G(ξ, θ) are collected by varying the valuesof only two variables, i.e., ξ and θ. In contrast, the three-dimensional(3-D) Fourier transform of the desired MBRF density f(x,y,γ) requires anappropriate set of projection measurements over three dimensions,whereas there exist only two in the current example. Therefore, theapplication of projection-slice methods is not applied to invert the MBtransformation equation (13) in the present embodiment. One skilled inthe art recognizes that the application of the projection-slice methodmay be used as an alternative to inversion in an alternative embodiment,wherein the data consists of three-dimensional projection measurements.

The second class of inversion methods used to reconstruct the SBreflectivity function θ_(SB)(x,y) utilizes back-projection functions.Back projection functions reconstruct ƒ_(SB)(x,y) based upon themeasured function-domain projection function g_(SB)(s, θ).Alternatively, a frequency-domain back-projection technique may be usedto reconstruct ƒ_(SB)(x,y). The present embodiment appliesback-projection techniques in order to reconstruct the MBRF ƒ(x,y,γ)based upon the phase history measurements G(ξ, θ).

Alternatively, for a continuous-domain solution, the inversion of the MBprojection equation begins with the following definition of theback-projection operation applied to the MB measured transform-domainprojections G(ξ, θ):

$\begin{matrix}{{b\left( {x,y,\gamma} \right)} \equiv {\int_{\theta\;\min}^{\theta\;\max}\ {{\mathbb{d}\theta}{\int_{\xi\;\min}^{\xi\;\max}\ {{\mathbb{d}\xi}\;{G\left( {\xi,\theta} \right)}{{\exp\left( {{{j2\pi}\;\left\lbrack {{x\;{\cos(\theta)}} + {y\;{\sin(\theta)}} + \gamma} \right\rbrack}\xi} \right)}.}}}}}} & (15)\end{matrix}$The limits of integration in Eq. (15) is chosen to be consistent withthe collected transform-domain measurements, i.e., ξ_(min)≦ξ≦ξ_(max) andθ_(min)≦θ≦θ_(max), as described above.

Substituting Eq. (13) for G(ξ, θ) into the back projection functionb(x,y,γ) of Eq. (15) and interchanging the order of integration gives

$\begin{matrix}{{b\left( {x,y,\gamma} \right)} = {\int_{\infty}^{\infty}\ {{\mathbb{d}x^{\prime}}{\int_{\infty}^{\infty}\ {{\mathbb{d}y^{\prime}}{\int_{\infty}^{\infty}\ {{\mathbb{d}\gamma^{\prime}}\;{f\left( {x^{\prime},y^{\prime},\gamma^{\prime}} \right)}{\int_{\theta\;\min}^{\theta\;\max}\ {{\mathbb{d}\theta}{\int_{\xi\;\min}^{\xi\;\max}{{\lambda\left( {{x - x^{\prime}},{y - y^{\prime}},{\gamma - \gamma^{\prime}}} \right)}.}}}}}}}}}}} & (16)\end{matrix}$in terms of the kernel function λ(x,y,γ) is defined by

$\begin{matrix}{{\lambda\left( {x,y,\gamma} \right)} \equiv {\int_{\theta\;\min}^{\theta\;\max}\ {{\mathbb{d}\theta}{\int_{\xi\;\min}^{\xi\;\max}{{\mathbb{d}\xi}\;{{\exp\left( {{{j2\pi}\left\lbrack {{x\;{\cos(\theta)}} + {y\;{\sin(\theta)}} + \gamma} \right\rbrack}\xi} \right)}.}}}}}} & (17)\end{matrix}$Thus, the MB back-projection function b(x,y,γ) can be expressed in termsof the MBRF ƒ(x,y,γ) viab(x,y,γ)=λ(x,y,γ)*ƒ(x,y,γ).  (18)In this equation, the symbol * denotes the 3-D convolution operation.The 3-D Fourier transform operator

₃ applied to both sides of Eq. (18) gives the following inversiontechnique for obtaining the MBRF ƒ(x,y,γ), given the transform-domainmeasurements G(ξ, θ):

f ⁡ ( x , y , γ ) . = 3 - 1 ⁢ { 3 ⁢ { b ⁡ ( x , y , γ ) } 3 ⁢ { λ ⁡ ( x , y ,γ ) } } ( 19 )Here, the operator

₃ ⁻¹ denotes the inverse Fourier transform in three dimensions. In Eq.(19), the back-projection function b(x,y,γ) is computed by inserting thetransform-domain measurement function G(ξ, θ) into Eq. (15). A solutionfor ƒ(x,y,γ) exists provided that the 3-D Fourier transform of thekernel λ(x,y,γ) has no zeros in the region-of-interest. This constraintinvolved in obtaining the MBRF solution is more transparent in thediscrete-domain, as shown in the following subsection.

SAR image reconstruction requires that the MBRF ƒ(x,y,γ) be determinedon a discrete grid in (x,y,γ) space, i.e., {x_(l)}_(l=1, . . . ,L),{y_(m)}_(m=1, . . . ,M), and {γ_(n)}_(n=1, . . . ,N). This grid is notrequired to have equal intervals between grid points in each of thethree dimensions of (x,y,γ)-space. However, the assumption of a uniformgrid will be shown to reduce the required computation time, even if eachof the three dimensions has a different resolution. This analysisgenerates an estimate of the MBRF matrix f(l,m,n), which is the discreteapproximation of the MBRF density ƒ(x,y,γ).

The measurements arising from the coherent processing of the receivedradar waveform are transduced at a finite set of discrete spatialfrequencies {ξ_(p)}_(p=1, . . . ,P) and a finite set of discreteobservation angles {θ_(q)}_(q=1, . . . ,Q) relative to the definedcoordinate system. This grid in (ξ_(p),θ_(q))-space is not required tobe uniform, just as the (x_(l),y_(m),γ_(n))-space of the MBRF matrixf(l,m,n) can be non-uniform. Thus, the measured phase history data aredetermined by the discrete domain measurement vector G(p,q) as anapproximation of the density G(ξ, θ).

Given the definitions for the vectors f(l,m,n) and G(p,q), the MBtransformation equation (13) has the discrete form

$\begin{matrix}{{{G\left( {p,q} \right)} = {\sum\limits_{l = 1}^{L}\;{\sum\limits_{m = 1}^{M}\;{\sum\limits_{n = 1}^{N}\;{{H\left( {l,m,n,p,q} \right)}{f\left( {l,m,n} \right)}}}}}},} & (20)\end{matrix}$in terms of the matrix of phase functionsH(l,m,n,p,q)=exp(−j2π[x _(l) cos(θ_(q))+y _(m)sin(θ_(q))+γ_(n)]ξ_(p)).  (21)

Equation (20) can be expressed in the basic form of the algebraicreconstruction problem

=

  (22)where

is the vector of unknowns of length U≡L×M×N, the variable

is a vector of measurements of length V≡P×Q and

is the U×V matrix of the vectors giving the projection of an (l,m,n)object space point onto (p,q) measurement space. The requirement thatV≧U must be satisfied in order to obtain a solution for

, otherwise, Eq. (22) is undetermined.

A method for obtaining a solution of Eq. (22) is via the pseudo-inverse

={

^(H)

}⁻¹

^(H)

,  (23)with the superscript

denoting the Hermitian transpose. The pseudo-inverse gives the uniqueleast-squares solution provided that the square matrix

^(H)

is non-singular. For cases in which the matrix

^(H)

is ill-conditioned, either diagonal loading or singular-valuedecomposition can be applied. In addition, related techniques, known tothose skilled in the art, can be applied for cases of additive noise.

The matrix

^(H)

is of size U×U, so that the computation of the matrix inverse in Eq.(23) is of order O(U³), which is the rate-determining step. However,techniques of order O(U×V) can be applied if the desired MBRF vectorf(l,m,n) is computed on a uniform grid in (x_(l),y_(m),γ_(n))-space. Todevelop these techniques, the back-projection vector is defined via

$\begin{matrix}{{b\left( {l,m,n} \right)} \equiv {\sum\limits_{p = 1}^{P}\;{\sum\limits_{q = 1}^{Q}\;{{H^{H}\left( {l,m,n,p,q} \right)}{{G\left( {p,q} \right)}.}}}}} & (24)\end{matrix}$Inserting Eq. (20) for G(p,q) into Eq. (24) and interchanging the orderof the summation gives

$\begin{matrix}{{{b\left( {l,m,n} \right)} = {\sum\limits_{l = 1}^{L}\;{\sum\limits_{m = 1}^{M}\;{\sum\limits_{n = 1}^{N}\;{\overset{\_}{\Lambda}\;\left( {l,l^{\prime},m,m^{\prime},n,n^{\prime}} \right){f\left( {l^{\prime},m^{\prime},n^{\prime}} \right)}}}}}},} & (25)\end{matrix}$in terms of the matrix kernel

$\begin{matrix}{{{{\overset{\_}{\Lambda}\left( {l,l^{\prime},m,m^{\prime},n,n^{\prime}} \right)} \equiv {\sum\limits_{p = 1}^{P}\;{\sum\limits_{q = 1}^{Q}\;{{H^{H}\left( {l,m,n,p,q} \right)}{H\left( {l,m,n,p,q} \right)}}}}} = {\sum\limits_{p = 1}^{P}\;{\sum\limits_{q = 1}^{Q}{\exp\left( {{j2}\;\pi{\overset{\_}{\Omega}\left( {l,l^{\prime},m,m^{\prime},n,n^{\prime}} \right)}} \right)}}}},} & (26)\end{matrix}$withΩ(l,l′,m,m′,n,n′)≡[{x _(l) −x _(l′)}cos(θ_(q))+{y _(m) −y_(m′)})sin(θ_(q))+{γ_(n)−γ_(n′)}]ξ_(p),  (27)For the case of a uniform grid in (x_(l),y_(m),γ_(n))-space, i.e.,{x_(l)=l δ_(x)}_(l=1, . . . ,L), {y_(m)=mδ_(y)}_(m=1, . . . ,M), and{γ_(n)=nδ_(γ)}_(n=1, . . . ,N), with δ_(x), δ_(y), δ_(γ), all constants,Eq. (27) reduces toΩ(l,l′m,m′,n,n′)=[{l−l′}δ_(x) cos(θ_(q))+{m−m′}δ_(y)sin(θ_(q))+{n−n′}δ_(γ)]ξ_(p),  (28)Thus, Eq. (25) has the formb(l,m,n)=Λ(l,m,n)*f(l,m,n)  (29)with

$\begin{matrix}{{{\Lambda\left( {l,m,n} \right)} \equiv {\sum\limits_{p = 1}^{P}\;{\sum\limits_{q = 1}^{Q}\;{\exp\left( {{j2\pi}\;{\Omega\left( {l,m,n,p,q} \right)}} \right)}}}},} & (30)\end{matrix}$and the phase matrixΩ(l, m, n, p, q)≡[lδ _(x) cos(θ_(q))+mδ _(y) sin(θ_(q))+nδ_(γ)]ξ_(p).  (31)Here, the symbol “*” denotes the 3-D discrete convolution. Theconvolutional structure of Eq. (29) permits the inversion of f(l,m,n) tobe computed in FFT-time of order O(U log₂(U)). Thus the rate-determiningstep is the computation of the back-projection vector b(l,m,n) given themeasurement phase history vector G(p,q) via Eq. (24), which is of orderO(U×V). Alternatively, function-domain back-projection operations can beperformed in order O(U log₂(U)), such that in an alternative embodimentof the present invention, the transform-domain back-projections appliedto MB image reconstruction described herein, can also can be implementedin order O(U log₂(U)).

The SB analog of Eq. (29) has the formb _(SB)(l,m)=Λ_(SB)(l,m)*f _(SB)(l,m),  (32)with

$\begin{matrix}{{\Lambda_{SB}\left( {l,m} \right)} \equiv {\sum\limits_{p = 1}^{P}\;{\sum\limits_{q = 1}^{Q}\;{{\exp\left( {{j2\pi}\;{\Omega_{SB}\left( {l,m,p,q} \right)}} \right)}.}}}} & (33)\end{matrix}$andΩ_(SB)(l, m, n, p, q)≡[lδ _(x) cos(θ_(q))+mδ _(y)sin(θ_(q))]ξ_(p).  (34)Here, the symbol “*” denotes two-dimensional (2-D) discrete convolution.In Eq. (32), b_(SB)(l,m) is determined by

$\begin{matrix}{{{b_{SB}\left( {l,m} \right)} = {\sum\limits_{p = 1}^{P}\;{\sum\limits_{q = 1}^{Q}\;{{H_{SB}^{H}\left( {l,m,n,p,q} \right)}{G\left( {p,q} \right)}}}}},} & (35)\end{matrix}$in terms of the matrix of SB phase functionsH _(SB)(l,m,p,q)=exp(−j2πΩ_(SB)(l, m, p, q)).  (36)Since the measurements G(p,q) are determined by the MB transformationEq. (20), the SB reconstruction technique of computing f(l, m) basedupon Eqs. (32)–(36) will contain undesirable artifacts unlesscontributions due to MB scattering events are absent in the measurementphase history data G(p,q).

The previous paragraphs have developed a technique for MB imagereconstruction by deriving the 3-D convolution equation (29), which isequivalent to the matrix form of Eq. (25). The existence of awell-behaved solution for the MBRF f(l, m, n) requires that the 3-DHermitian-Toeplitz matrix Λ(l,l′,m,m′,n,n′)=Λ(l−l′, m−m′, n−n′) benon-singular. The constraint is satisfied if the vectorsh_(l, m, n)(p,q)≡H(l,m,n,p,q) are all linearly independent functions ofthe phase history measurement indices p and q. This constraint issatisfied if one or more of the elements of the normalized phase vectorsa_(l, m, n)(p,q)≡Ω(l,m,n,p,q) of Eq. (31) change by unity or greaterbetween adjacent cells in image-space (l,m,n). The normalized phasevectors a_(l, m, n)(p,q) satisfy this requirement, and thus yield awell-behaved solution to Eq. (29) or (25), if the following conditionsapply.

First, consider the fact that spotlight SAR phase history data istypically collected over a narrow extent of measurement anglesΔ_(θ)≡θ_(max)−θ_(min)<<1, here considered about θ=0 without loss ingenerality. For a power-series expansion about θ=0 through only firstorder in θ, the factor cos(θ_(q)) in the normalized phase of Eq. (31) isapproximately unity. Thus, the dependence of the along-range coordinatex in Eq. (31) is identical to that of the MB coordinate γ, so that theeffects of x and γ cannot be differentiated through first order.Therefore, a well-behaved MB reconstruction requires that the extent ofmeasurement angles Δ_(θ) be sufficiently large that second order effectsbecome significant. The required second order expansion of Eq. (31) hasthe form:

$\begin{matrix}{{a_{l,m,n}\left( {p,q} \right)} \equiv {\Omega\left( {l,m,n,p,q} \right)} \cong {\left\lbrack {{\left\{ {{l\;\delta_{x}} + {n\;\delta_{\gamma}}} \right\} m\;\delta_{y}\theta_{q}} - {\frac{1}{2}l\;\delta_{x}\theta_{q}^{2}}} \right\rbrack\xi_{p}}} & (37)\end{matrix}$

In order to give a desired image-space resolution (δ_(x), δ_(y), δ_(γ)),the extent of measurements in spatial frequency Δ_(ξ) and in angle Δ₇₄must be selected to be large enough so that the zeroth, first, andsecond order terms in Eq. (37) all change by unity or greater betweenadjacent image-space cells (l,m,n). The change in the zeroth order phaseterm {lδ_(x)+nδ_(γ)}ξ_(p) of Eq. (37) exceeds unity between adjacentcells in both the x and γ dimensions over the extent of spatialfrequencies ifΔ_(ξ)≳max{δ_(x) ⁻¹, δ_(γ) ⁻¹},  (38)which reduces to the well-known SB relation Δ_(ξ)˜δ_(x) ⁻¹.

The change in the first order phase term mδ_(y)θ_(q)ξ_(p) of Eq. (37)exceeds unity between adjacent cells in they dimension over the extentof measurement angles ifΔ₇₄≳{ξ_(cƒ)δ_(y)}⁻¹,  (39)with ξ_(cƒ) equal to the center spatial frequency. This relation isidentical to that of the SB case. The change in the second order phaseterm—(½)lδ_(xθ) _(q) ₂ ξ_(p) of Eq. (37) exceeds unity between adjacentcells in the x dimension over the extent of measurement angles ifΔ_(θ)≳√{square root over (2{ξ_(cƒ)δ_(x)}⁻¹)},  (40)Thus, the first and second order constraints of Eqs. (39) and (40) arecombined to yieldΔ_(θ)≳max {{ξ_(cƒ)δ_(y)}⁻¹, √{square root over(2{ξ_(cƒ)δ_(x)}⁻¹)}}  (41)For the case of identical image-space resolutions in each dimension,i.e., δ≡δ_(x)=δ_(y)=δ_(γ), the required measurement extents reduce toΔ_(ξ)≳δ⁻¹  (42)Δ_(θ)≳√{square root over (2{ξ_(cƒ)δ}⁻¹)}.  (43)Therefore, the extent of measurement frequencies required for the MBcase is unmodified from that of the SB case. However, the extent ofmeasurement angles is greater for the MB case than for the SB case.

In order to derive an expression for the measurement intervals δ_(ξ) andδ_(θ) required for a desired image-space extent (Δ_(x)≡Lδ_(x),Δ_(y)≡Mδ_(y), Δ_(γ)≡Nδ_(γ), consider the normalized phase of Eq. (37)with θ_(q)=_(q)δ_(θ) and ξ_(p)=ξ_(cƒ)+pδ_(ξ), i.e.,

$\begin{matrix}{{\Omega\left( {l,m,n,p,q} \right)} \cong {{\left\{ {{l\;\delta_{x}} + {n\;\delta_{\gamma}}} \right\}\left\{ {\xi_{cf} + {p\;\delta_{\xi}}} \right\}} + {m\;\delta_{\gamma}q\;\delta_{\theta}\left\{ {\xi_{cf} + {p\;\delta_{\xi}}} \right\}} - {\frac{1}{2}l\;\delta_{x}q^{2}\delta_{\theta}^{2}{\left\{ {\xi_{cf} + {p\;\delta_{\xi}}} \right\}.}}}} & (44)\end{matrix}$The change in the normalized phase of Eq. (44) must be unity or lessbetween adjacent measurement samples δ_(ξ) and δ_(θ) in order to obtaina well-behaved solution. The change in the zeroth order term of Eq. (44)is unity or smaller over the image-space extents in the x and ydimensions ifδ_(ξ)≲{Δ_(x)+Δ_(y)}⁻¹.  (45)The first order term of Eq. (44) is unity or smaller over theimage-space extents in they dimension ifδ_(θ)≲{Δ_(x)+Δ_(y)}⁻¹.  (46)whereas the second order term does so forδ_(ξ)≲√{square root over (2{ε_(cƒ)Δ_(x)}⁻¹)}.  (47)The combination Eqs. (46) and (47) givesδ_(θ)≲min{{ξ_(cƒ)Δ_(y)}⁻¹,√{square root over (2{ξ_(cƒ)Δ_(x)}⁻¹)}},  (48)For equal extents in the x and y spatial dimensions, i.e.,Δ≡Δ_(x)=Δ_(y), Eq. (48) reduces toδ_(θ)≲{ξ_(cƒ)Δ}⁻¹,  (49)which is identical to that of the SB case.

The SAR image reconstruction processes developed above are applied tosimulated MB phase history data with the following results. FIG. 2 apresents an example containing a number of assumed scattering centerreflectivities. The co-located transmitter and receiver (not shown) arelocated at the top of each of the figures, transmitting and receivingdown and up the paper in a lengthwise direction. This plot correspondsto the γ=0 plane of the MBRF ƒ(x,y,γ). In addition, it is assumed thatthere exists a MB coupling within the four sets of closely-spacedscattering centers located in the four corners 12, 14, 16, and 18 ofFIG. 2 a. For example, there is a contribution to the MBRF ƒ(x,y,γ)corresponding to the two scattering centers at (x,y)=(12, 3) and (12, 5)located in corner 12 of the truth plot in FIG. 2 a. Specifically, theγ=1 plane of the MBRF shown in FIG. 2 b contains a contribution at themean position (x,y)=(12, 4) between these two scattering centers, inaccord with the distance 2γ=2 between these two scattering centers.Likewise, the truth scattering centers at (x,y)=(12, 13) and (14, 13) inthe lower right corner 14 of FIG. 2 a yield the γ=1 MB contribution at(13, 13) in FIG. 2 b. Similarly, the truth scattering centers at(x,y)=(3, 5) and (5, 3) in the upper left corner 16 of FIG. 2 a have aMB coupling length of 2γ=2√{square root over (2)}, thus givinglinearly-interpolated contributions at (4, 4) in the γ=1 and γ=2 planesof FIGS. 2 b and 2 c.

The three coupled scattering centers at (x,y)=(3, 12), (5, 12) and (5,14) in the upper right corner 18 of FIG. 2 a give three differentdouble-bounce contributions corresponding to (x,y,γ)=(4, 12, 1), (5, 13,1), and (4, 13, √{square root over (2)}), similar to the double-bouncecouplings in the other three corners 12, 14, and 16 of FIG. 2 a. Inaddition, the upper right corner 18 scattering centers yield varioustriple-bounce events. For example, the scattering sequence (5, 14)→(5,12)→(3, 12) gives the MB contribution at (x,y,γ)=(4, 13, 2) in FIG. 2 c,since the total path length between the initial point (5, 14) and thefinal point (3, 12) is 2γ=4 Likewise, the scattering sequence (5,14)→(3, 12)→(5, 12) gives a contribution for (x,y,γ)=(5, 13, √{squareroot over (2)}+1), and the sequence (3, 12)→(5, 14)→(5, 12) yields(x,y,γ)=(4, 12, √{square root over (2)}+1).

The MBRF ƒ(x,y,γ) for γ≠0 in FIGS. 2 b–2 d is based upon the true pathlengths between the scattering centers of FIG. 2 a However, the couplingstrengths between the scattering centers were selected in an ad hocmanner to be equal to that of the truth scattering centers themselves.Nevertheless, the MB techniques described herein apply generically forany specific quantitative rules governing the MB coupling strengths.

The true values of the MBRF ƒ(x,y,γ) shown in FIGS. 2 a–2 d are appliedwithin Eq. (20) in order to simulate the measurement function G(ξ, θ)shown in FIGS. 3 a and 3 b. The measurement functions shown in FIGS. 3 aand 3 b are coherent measurements, resulting from a quadraturedemodulation of the received waveform, i.e., the reflected wave form.The x-axis is the measure of angle in radians, while the y, axis isspatial frequency. The measurement functions of FIGS. 3 a and 3 b, wheninverted using the single bounce theory, give the result shown in FIG.4. These same measurement functions, when inverted using the process ofthe present invention, give the results discussed below with referenceto FIGS. 5 a–5 d, creating not only an identical replica of the desiredimage but, also creating higher delay planes or multiple bounce imageplanes that contain the information rich echo portions of the reflectedwave form The signal-to-noise ratio in this embodiment is approximately40 dB. As stated above, noise and interference play no greater a role inthe IRAMS processing than in the single bounce processing. Consequently,one skilled in the art recognizes the standard signal-to-noiselimitations that are applicable to IRAMS processing.

The results of Eq. (37)–(49) are used to determine the required samplingintervals and extents of both the spatial frequency ξ and the angularcoordinate θ which are necessary to reconstruct the truth MBRF ƒ(x,y,γ)of FIGS. 2 a–2 d. The desired image-space resolution isδ≡δ_(x)=δ_(y)δ_(γ)=1, so that Eq. (42) implies that a spatial frequencybandwidth of Δ_(ξ)=1/δ is sufficient for a well-behaved MBreconstruction. SAR measurements are collected over a narrow extent ofmeasurement angles, which is chosen to be Δ_(θ)˜0.2 rad˜11.5° in thisparticular example. Then the spatial center frequency is selected to be

${\xi_{cf} = {{{2/\Delta}\frac{2}{\theta}} \cong 45}},$consistent with δ=1 and Δ_(θ)≅0.2 rad in Eq. (43). The desiredreconstruction extents are given by Δ_(x)=Δ_(y)=16 and Δ_(γ)=4 for thisexample. Thus, Eq. (45) implies that the required sampling interval inspatial frequency is δ_(ξ)= 1/20, and Eq. (49) gives a required angularsampling interval of δ_(θ)≅1/720 rad≅0.08°.

FIGS. 5 a–5 d show the application of the MB inversion of Eqs. (24) and(29) in order to estimate the MBRF ƒ(x,y,γ) based upon the simulatedphase history data G(ξ, θ) of FIGS. 3 a and 3 b. A shown, they γ=0, γ=1,γ=2, and γ=3 planes of FIGS. 5 a–5 d give an approximately identicalreconstruction of the desired scattering center locations and strengthsof FIG. 2 a. Further, the extracted MB scattering events located withinthe γ≠0 planes can be used to augment the feature vectors applied to,for example, target recognition analysis.

For the sake of comparison, the desirable MB image reconstruction ofFIGS. 5 a–5 d is compared with that based upon the assumption of only SBscattering events. FIG. 4 presents the result of the SB inversiontechnique of Eqs. (32) and (35) applied to the simulated MB phasehistory data of FIGS. 3 a and 3 b. Clearly, the SB technique reveals asignificant degradation in the estimation of the object scatteringcenters of FIG. 2 a. Although the object locations and strengths of thevarious scattering centers of FIG. 2 a are well-reproduced, FIG. 4contains many additional spurious scattering centers that arise from theMB contributions to the simulated phase history data of FIGS. 3 a and 3b. Whereas the MB inversion technique accurately reconstructs a given MBscattering event characterized by (x,y,γ), the SB inversion methodincorrectly projects such a point to (x+γ, y) for phase history datameasured over a narrow angular extent about θ=0. For example, the MBscattering event of (x,y,γ)=(12, 4, 1) of FIGS. 2 b is incorrectlyprojected to the point (x,y)=(13, 4) within the SB estimate of FIG. 4and similar incorrect results apply to the other MB scattering events.

The ability to form a well-behaved MB inversion depends entirely uponthe coordinate grids {x_(l), y_(m), γ_(n)} and {ξ_(p), θ_(q)} selectedfor the desired MBRF f(l,m,n) and the measurements G(p,q). That is, thespecific number of scattering centers and their MB coupling strengthswithin the true MBRF are irrelevant to the inversion process, so thatexamples containing a much higher density of scattering events can alsobe generated. The particular example presented herein was selected inorder to demonstrate the most salient effects in the MB and SB imagereconstructions.

The embodiment described above may be extended to include data from ahigher number of spatial dimensions. Define r to be a vector in an

-dimensional vector space. Likewise, define k to be a unit vector inthis

-D vector space. The

=2 case presented above characterizes the spatial vector viar≡[x,y]^(T), with the superscript denoting vector transpose. Similarly,the unit vector for

=2 is expressed in terms of the polar coordinate angle θ, i.e.,k≡[cos(θ), sin(θ)]^(T). Likewise, the

=3 case can be developed by defining the spatial vector r≡[x,y,z]^(T)and the corresponding unit vector k≡[cos(θ) sin(ø), sin (θ) sin(ø),cos(ø)]^(T) in terms of the spherical coordinate angles ø and θ.

The generic

-D form of the MB projection equations (7) isg(s, k)≡∫dr∫dγƒ(r, γ)δ(k ^(T) r+γ−s).  (50)

The corresponding

-D form of the MB transform-domain projection equation (13) becomesG(ξ, k)=∫drƒ(r, γ)exp(−j2π{k ^(T)r+γ}ξ).  (51)

In a similar fashion, the

-D form of the MB back-projection equation (15) isb(r, γ)≡∫dk G(ξ, k) exp(j2π{k ^(T) r+γ}ξ).  (52)This

-D form of the back-projection function b(r, γ) can be expressed in thefollowing convolutional formb(r, γ)=λ(r, γ)*ƒ(r,γ),  (53)in terms of the kernel functionλ(r, γ)≡∫dk∫dξexp(j2π{k ^(T) r+γ}ξ)  (54)Thus, the inversion for the MBRF yields

f ⁡ ( r , γ ) = = 1 1 ⁢ { + 1 ⁢ { b ⁡ ( r , γ ) } + 1 ⁢ { λ ⁡ ( r , γ ) } } .( 55 )A similar set of generic

-D equations can be developed in the discrete domain.

The multiple-bounce inversion technique yields excellent reconstructionsof the multiple-bounce reflectivity function ƒ(x,y,γ). In fact, thesemultiple-bounce inversion methods will not degrade the reconstructedimage for cases in which the phase history measurements G(ξ, θ) containonly single-bounce contributions. In contrast, the single-bounceinversion methods of conventional spotlight SAR theory yield spuriousghosting artifacts in the image reconstruction ƒ_(SB)(x,y) for cases inwhich multiple-bounce events contribute to the measured phase historydata G(ξ, θ).

In an alternate embodiment of the present invention, FIGS. 6 a–6 d referto truth reflectivity functions identical to those shown in FIGS. 3 a–3d. FIGS. 7 a–7 b, similar to FIGS. 4 a–4 b, show the measurement datafor G(ξ, θ) corresponding to the given ƒ(x,y,γ). The measurement datashown in FIGS. 7 a–7 b differs from that shown in FIGS. 4 a–4 b, in thatthe data is collected at low spatial frequencies from about ξ=0.5 toξ=1.5 as opposed to the much higher spatial frequencies in FIGS. 4 a–4 bwhich is from ξ=45.1 to ξ=46.1. Inversion of the measurement data usingthe MB reconstruction process described herein results in a virtuallyidentical replica depicted in FIGS. 9 a–9 d of the truth reflectivityfunction shown in FIGS. 6 a–6 d. Whereas, the inversion of themeasurement data using the SB reconstruction process shown in FIG. 8contains significant degradation due to the appearance of ghostscattering centers. The signal-to-noise ratio in this embodiment isapproximately 40 dB. As stated above, noise and interference play nogreater a role in the IRAMS processing than in the single bounceprocessing. Consequently, one skilled in the art recognizes the standardsignal-to-noise limitations that are applicable to IRAMS processing.

The IRAMS process described herein is also applicable to near-fieldcollection data, wherein the wavefronts are curved. For the 2-D problemabove, assume that the radius and angle (in polar coordinates) of thesensor are given by R(η) and φ(η), respectively, in terms of a scalarparameter τ which can vary in any fashion with regard to the attributesof the scene of interest. Then the measured range-to-target with respectto the coordinate origin is

$\begin{matrix}{{s\left( {{R(\eta)},{\phi(\eta)}} \right)} = {{\frac{1}{2}\sqrt{\left\lbrack {{{R(\eta)}{\cos\left( {\phi(\eta)} \right)}} - x_{i}} \right\rbrack^{2} + \left\lbrack {{{R(\eta)}{\sin\left( {\phi(\eta)} \right)}} - y_{i}} \right\rbrack^{2}}} + {\frac{1}{2}\sqrt{\left\lbrack {{{R(\eta)}{\cos\left( {\phi(\eta)} \right)}} - x_{f}} \right\rbrack^{2} + \left\lbrack {{{R(\eta)}{\sin\left( {\phi(\eta)} \right)}} - y_{f}} \right\rbrack^{2}}} + \gamma - {R(\eta)}}} & (56)\end{matrix}$This equation replaces Eq. (1) for the near-field problem. The remainderof the analysis is straightforward, wherein the integrations/summationsover θ are replaced by the corresponding integrations/summations overthe scalar parameter η. It can also be verified by a Taylor seriesexpansion that the near-field equation (56) reduces to the far-fieldequation (1) as the radius R(η) becomes much larger than the size of thescene of interest. Equation (56) can be further extended to apply for anon-planar collection surface by defining the sensor location in termsof the spherical coordinates (R(η), φ(η), ω(η)) parameterized by thescalar parameter η via

$\begin{matrix}{{s\left( {{R(\eta)},{\phi(\eta)},{\phi(\eta)}} \right)} = \mspace{25mu}{{\frac{1}{2}\sqrt{\begin{matrix}{\left\lbrack {{R(\eta){\cos\left( {\phi(\eta)} \right)}{\sin\left( {\omega(\eta)} \right)}} - x_{i}} \right\rbrack^{2} +} \\{\left\lbrack {{R(\eta){\sin\left( {\phi(\eta)} \right)}{\sin\left( {\omega(\eta)} \right)}} - y_{i}} \right\rbrack^{2} + \left\lbrack {{{R(\eta)}{\cos\left( {\omega(\eta)} \right)}} - z_{j}} \right\rbrack^{2}}\end{matrix}}} + {\frac{1}{2}\sqrt{\begin{matrix}{\left\lbrack {{R(\eta){\cos\left( {\phi(\eta)} \right)}{\sin\left( {\omega(\eta)} \right)}} - x_{f}} \right\rbrack^{2} +} \\{\left\lbrack {{R(\eta){\sin\left( {\phi(\eta)} \right)}{\sin\left( {\omega(\eta)} \right)}} - y_{f}} \right\rbrack^{2} + \left\lbrack {{{R(\eta)}{\cos\left( {\omega(\eta)} \right)}} - z_{f}} \right\rbrack^{2}}\end{matrix}^{2}}} + \gamma - {R(\eta)}}} & (57)\end{matrix}$This type of modeling also permits targets to have different heights z.

Further, the location of the transmitter and receiver can be different.The required modifications for such a bistatic collection are well-knownto those skilled in the art. There are two primary modificationsrequired. First, the measurement angle θ for a two-dimensionalmonostatic collection (wherein the transmitter and receiver areco-located) in the far-field is replaced by the bisector angle betweenthe angle of the transmitter and that of the receiver. Second, themeasured spatial frequencies for this problem are scaled by the factorcos(β/2), with β equal to the angle between that of the transmitter andthat of the receiver. Further extension of the bistatic problem to thenear-field case and to a non-planar collection surface, as presentedabove for the monostatic case, can be accomplished by one who is skilledin the art.

The specific examples described above are not intended to be limiting.Alteration of the processes described above is contemplated by oneskilled in the art for application of the IRAMS process to various typesof reflection tomographic data, including radar, sonar, and the like.

1. A process for analyzing radar scattering data from anarea-of-interest comprising: receiving radar scattering data from thearea-of-interest, the area-of-interest includes at least two scatteringpoints; identifying the radar scattering data as containingmultiple-bounce information resulting from reflection off of the atleast two scattering points; imaging the area-of-interest using themultiple-bounce information; and separating the radar scattering dataaccording to the identifying the radar scattering data, whereinseparating the radar scattering data comprises: reconstructing the atleast two scattering points according to the identifying the radarscattering data, wherein the reconstructing the at least two scatteringpoints comprises: reconstructing a multiple bounce reflectivity function(“MBRF”)ƒ(x,y,γ) for each of the at least two scattering points based onmeasured phase history data as described according to the equationG(ξ, θ) ≡ ∫_(∞)^(∞) 𝕕x∫_(∞)^(∞) 𝕕y∫_(∞)^(∞) 𝕕γ f(x, y, γ)exp (−j2π[x cos (θ) + y sin (θ) + γ]ξ),wherein ξ represents spatial frequency and θ represents measurementangle.
 2. A process for analyzing radar scattering data from anarea-of-interest comprising: receiving radar scattering data from thearea-of-interest, the area-of-interest includes at least two scatteringpoints; identifying the radar scattering data as containingmultiple-bounce information resulting from reflection off of the atleast two scattering points; imaging the area-of-interest using themultiple-bounce information; and separating the radar scattering dataaccording to the identifying the radar scattering data, wherein theseparating the radar scattering data comprises: reconstructing the atleast two scattering points according to the identifying the radarscattering data, wherein the reconstructing the at least two scatteringpoints comprises: reconstructing a multiple bounce reflectivity function(“MBRF”)ƒ(x,y,γ) for each of the at least two scattering points based onmeasured phase history data as described according to the equationG(ξ^(′), θ^(′)) ≡ ∫_(∞)^(∞) 𝕕x∫_(∞)^(∞) 𝕕y∫_(∞)^(∞) 𝕕γ f(x, y, γ)exp (−j2π[x cos (θ^(′)) + y sin (θ^(′)) + γ]ξ^(′)),wherein ξ′ represents spatial frequency which is scaled by a factorcos(β/2), β being equal to an angle between the first location and thesecond location, and θ′ is a bisector angle between the angle β;transmitting at a first location a radar signal toward thearea-of-interest; and receiving radar scattering data includes receivingat a second location the radar scattering data from a reflection of theradar signal, the second location is different from the first location.